Uma nova abordagem de padrões binários em radiografias de tórax para avançar o diagnóstico de tuberculose

Autores

  • Afonso Ueslei da Fonseca da Fonseca Universidade Federal de Goiás
  • Emilia Alves Nogueira Universidade Federal de Goiás
  • Ana Luisa de Bastos Chagas Universidade Federal de Goiás
  • Juliana Paula Felix Universidade Federal de Goiás
  • Deborah Silva Alves Fernandes Universidade Federal de Goiás
  • Fabrizzio Soares Universidade Federal de Goiás

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1349

Palavras-chave:

Diagnóstico, Inteligência Artificial, Tuberculose

Resumo

Objetivo: A tuberculose (TB) afeta milhões de pessoas, principalmente as mais miseráveis, revelando desigualdades sociais. Apesar dos avanços da inteligência artificial (IA) no controle da TB, poucos benefícios chegam aos mais necessitados. Este estudo propõe uma IA otimizada para discriminar casos de TB de indivíduos saudáveis. Método: A abordagem incorpora descritores por congruência de fase e padrões binários locais em um modelo de otimização mínima sequencial (SMO) na análise de radiografias de tórax (RXT). Resultados: A IA otimizada apresenta desempenho superior a abordagens existentes na literatura, entregando valor de especificidade superior a 97% em diferentes bases e cenários de segmentação. Conclusão: A aplicação da IA proposta na análise de RXT pode representar um avanço significativo no controle da TB, especialmente em populações mais necessitadas, pois constitui uma solução acessível e eficaz que  abre possibilidades para o desenvolvimento de novos sistemas de apoio ao diagnóstico.

Biografias Autor

Afonso Ueslei da Fonseca da Fonseca, Universidade Federal de Goiás

Doutor(a), Instituto de Informática, Universidade Federal de Goiás, GO, Brasil.

Emilia Alves Nogueira, Universidade Federal de Goiás

Doutoranda, Instituto de Informática, Universidade Federal de Goiás, GO, Brasil.

Ana Luisa de Bastos Chagas, Universidade Federal de Goiás

Graduanda, Instituto de Informática, Universidade Federal de Goiás, GO, Brasil.

Juliana Paula Felix, Universidade Federal de Goiás

 Doutor(a), Instituto de Informática, Universidade Federal de Goiás, GO, Brasil.

Deborah Silva Alves Fernandes, Universidade Federal de Goiás

Doutor(a), Instituto de Informática, Universidade Federal de Goiás, GO, Brasil.

Fabrizzio Soares, Universidade Federal de Goiás

Doutor(a), Instituto de Informática, Universidade Federal de Goiás, GO, Brasil.

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Publicado

2024-11-19

Como Citar

da Fonseca, A. U. da F., Nogueira, E. A., Chagas, A. L. de B., Felix, J. P., Fernandes, D. S. A., & Soares, F. (2024). Uma nova abordagem de padrões binários em radiografias de tórax para avançar o diagnóstico de tuberculose. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1349

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